Kinetic Intersections as a Source of Information on Rate Coefficients

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A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Framework for a space shuttle main engine health monitoring system

A framework developed for a health management system (HMS) which is directed at improving the safety of operation of the Space Shuttle Main Engine (SSME) is summarized. An emphasis was placed on near term technology through requirements to use existing SSME instrumentation and to demonstrate the HMS during SSME ground tests within five years. The HMS framework was developed through an analysis of SSME failure modes, fault detection algorithms, sensor technologies, and hardware architectures. A key feature of the HMS framework design is that a clear path from the ground test system to a flight HMS was maintained. Fault detection techniques based on time series, nonlinear regression, and clustering algorithms were developed and demonstrated on data from SSME ground test failures. The fault detection algorithms exhibited 100 percent detection of faults, had an extremely low false alarm rate, and were robust to sensor loss. These algorithms were incorporated into a hierarchical decision making strategy for overall assessment of SSME health. A preliminary design for a hardware architecture capable of supporting real time operation of the HMS functions was developed. Utilizing modular, commercial off-the-shelf components produced a reliable low cost design with the flexibility to incorporate advances in algorithm and sensor technology as they become available

Generalized pressure drop and heat transfer correlations for jet impingement cooling with jet adjacent fluid extraction

2022 Spring.Includes bibliographical references.Jet impingement technologies offer a promising solution to thermal management challenges across multiple fields and applications. Single jets and conventional impinging arrays have been studied extensively and are broadly recognized for achieving extraordinary local heat transfer coefficients. This, in combination with the versatility of impinging arrays, has facilitated a steady incline in the popularity of jet impingement investigations. However, it is well documented that interactions between adjacent jets in an impinging array have a debilitating effect on thermal performance. Recently, in an attempt to mitigate the jet interference problem, a number of researchers have created innovative jet impingement solutions which eliminate crossflow effects by introducing fluid extraction ports interspersed throughout the impinging array. This novel adaptation on classical impinging arrays has been shown to produce dramatically improved thermal performance and offers an excellent opportunity for future high-performing thermal management devices. The advent of jet-adjacent fluid extraction in impinging arrays presents a promising improvement to impingement cooling technologies. However, there have been very few investigations to quantify these effects. Notably, the current archive of literature is severely lacking in useful, predictive correlations for heat transfer and pressure drop which can reliably describe the performance of such impinging arrays. Steady-state heat transfer and adiabatic pressure drop experiments were conducted using nine unique geometric configurations of a novel jet impingement device developed in this work. This investigation proposes novel empirical correlations for Darcy friction factor and Nusselt number in an impingement array with interspersed fluid extraction ports. The correlations cover a broad range of geometric parameters, including non-dimensional jet array spacing (S/Dj) ranging from 2.7 to 9.1, and non-dimensional jet heights (H/Dj) ranging from 0.31 to 4.4. Experiments included jet Reynolds numbers ranging from 70 to 24,000, incorporating laminar and turbulent flow regimes. Multiple fluids were tested with Prandtl numbers ranging from 0.7 to 21. The correlations presented in this work are the most comprehensive to date for impinging jet arrays with interspersed fluid extraction. Nusselt number was found to be correlated to impinging jet Reynolds number to the power of 0.57. The resulting correlation was able to predict 93% of experimental data within ±25%. During adiabatic pressure drop experiments, multiple laminar-turbulent flow transition regions were identified at various stages in the complex jet impingement flow path. The proposed Darcy friction factor correlation was separated into laminar, turbulent, and transition regions and predicted experimental data with a mean absolute deviation of 20%. The heat transfer and pressure drop correlations proposed in this investigation were used in a follow-on optimization study which targeted an exemplary impingement cooling application. The optimization study applied core experimental findings to a microchip cooling case study and evaluated the effects of geometry, flow, and heat load parameters on cooling efficiency and effectiveness. It was discovered that reducing non-dimensional jet height results in all-around improved cooling performance. Conversely, low non-dimensional jet spacing results in highly efficient but less effective solutions while high non-dimensional jet spacing results in effective but less efficient cooling

Fractal Analysis and Chaos in Geosciences

The fractal analysis is becoming a very useful tool to process obtained data from chaotic systems in geosciences. It can be used to resolve many ambiguities in this domain. This book contains eight chapters showing the recent applications of the fractal/mutifractal analysis in geosciences. Two chapters are devoted to applications of the fractal analysis in climatology, two of them to data of cosmic and solar geomagnetic data from observatories. Four chapters of the book contain some applications of the (multi-) fractal analysis in exploration geophysics. I believe that the current book is an important source for researchers and students from universities

Machine Learning Methods for Image Analysis in Medical Applications, from Alzheimer\u27s Disease, Brain Tumors, to Assisted Living

Healthcare has progressed greatly nowadays owing to technological advances, where machine learning plays an important role in processing and analyzing a large amount of medical data. This thesis investigates four healthcare-related issues (Alzheimer\u27s disease detection, glioma classification, human fall detection, and obstacle avoidance in prosthetic vision), where the underlying methodologies are associated with machine learning and computer vision. For Alzheimer’s disease (AD) diagnosis, apart from symptoms of patients, Magnetic Resonance Images (MRIs) also play an important role. Inspired by the success of deep learning, a new multi-stream multi-scale Convolutional Neural Network (CNN) architecture is proposed for AD detection from MRIs, where AD features are characterized in both the tissue level and the scale level for improved feature learning. Good classification performance is obtained for AD/NC (normal control) classification with test accuracy 94.74%. In glioma subtype classification, biopsies are usually needed for determining different molecular-based glioma subtypes. We investigate non-invasive glioma subtype prediction from MRIs by using deep learning. A 2D multi-stream CNN architecture is used to learn the features of gliomas from multi-modal MRIs, where the training dataset is enlarged with synthetic brain MRIs generated by pairwise Generative Adversarial Networks (GANs). Test accuracy 88.82% has been achieved for IDH mutation (a molecular-based subtype) prediction. A new deep semi-supervised learning method is also proposed to tackle the problem of missing molecular-related labels in training datasets for improving the performance of glioma classification. In other two applications, we also address video-based human fall detection by using co-saliency-enhanced Recurrent Convolutional Networks (RCNs), as well as obstacle avoidance in prosthetic vision by characterizing obstacle-related video features using a Spiking Neural Network (SNN). These investigations can benefit future research, where artificial intelligence/deep learning may open a new way for real medical applications